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--- |
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title: Video Transcript Chatbot |
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emoji: 🎥 |
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colorFrom: yellow |
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colorTo: indigo |
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sdk: gradio |
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python_version: '3.10' |
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--- |
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# Video Transcript Chatbot |
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A beginner-friendly Gradio app that turns any YouTube video into a conversational chatbot using LangChain and Hugging Face Inference API. |
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## Features |
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- **Dynamic Video Input**: Paste a full YouTube URL or raw video ID. |
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- **Embedding Model Selection**: Pick any HF embedding model (default: `sentence-transformers/all-MiniLM-L6-v2`). |
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- **LLM Model Selection**: Choose any HF text-generation model (default: `meta-llama/Llama-3.1-8B-Instruct`). |
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- **Secure Token Entry**: You must enter your own HF API token at runtime—no hard-coded defaults. |
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- **Conversational Memory**: Multi-turn chat history is preserved. |
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- **Retrieval-Augmented Generation**: Uses FAISS + transcript context to ground answers. |
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--- |
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## Prerequisites |
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- **Python 3.8+** |
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- **Hugging Face API Token** with Inference access: |
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https://huggingface.co/settings/tokens |
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- **Git** (for cloning the repo) |
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--- |
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## Installation |
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1. **Clone the repo** |
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```bash |
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git clone https://github.com/<your-username>/yt-rag-chatbot.git |
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cd yt-rag-chatbot |
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2. **(Optional) Create a virtual environment** |
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```bash |
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python -m venv venv |
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source venv/bin/activate # macOS/Linux |
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venv\Scripts\activate # Windows |
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3. **Install dependencies** |
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```bash |
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python -m venv venv |
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source venv/bin/activate # macOS/Linux |
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venv\Scripts\activate # Windows |
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**Usage** |
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1. **Start the app:** |
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```bash |
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python app.py |
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2. **Open** your browser at the local URL (e.g. http://127.0.0.1:7860) |
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3. **Use the UI:** |
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- **YouTube Video URL or ID:** Paste your link/ID. |
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- **Embedding Model:** Leave default or enter another HF embedding model. |
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- **LLM Model:** Enter your desired HF LLM repo. |
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- **Your HF API Token:** Paste your token (input hidden). |
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- Click **Initialize Chat** to load and index the transcript. |
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- Ask questions in the chat window to interact with the video content. |
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**Customization** |
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- **Default Models:** Edit the default values for embedding_model_input and llm_model_input in app.py. |
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- **Retrieval Size:** Change the k value in the retriever configuration: |
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```python |
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retriever = vector_store.as_retriever(search_kwargs={'k': 4}) |
